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Found 29 papers, 12 papers with code
Modeling Edge Features with Deep Bayesian Graph Networks
We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features.
Is Rewiring Actually Helpful in Graph Neural Networks?
Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features.
Addressing Heterophily in Node Classification with Graph Echo State Networks
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node's neighbourhood.
Ranked #1 on
Node Classification
on genius
(1:1 Accuracy metric)
Leave Graphs Alone: Addressing Over-Squashing without Rewiring
Recent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-squashing' phenomenon.
Beyond Homophily with Graph Echo State Networks
Graph Echo State Networks (GESN) have already demonstrated their efficacy and efficiency in graph classification tasks.
Ranked #5 on
Node Classification
on Pubmed Full-supervised
Dynamic Graph Echo State Networks
Dynamic temporal graphs represent evolving relations between entities, e. g. interactions between social network users or infection spreading.
The Infinite Contextual Graph Markov Model
The Contextual Graph Markov Model is a deep, unsupervised, and probabilistic model for graphs that is trained incrementally on a layer-by-layer basis.
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum.
Phase Transition Adaptation
Artificial Recurrent Neural Networks are a powerful information processing abstraction, and Reservoir Computing provides an efficient strategy to build robust implementations by projecting external inputs into high dimensional dynamical system trajectories.
Pyramidal Reservoir Graph Neural Network
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers.
Graph Mixture Density Networks
We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology.
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs
The method relies on deep graph networks, which provide extreme flexibility in the input format.
Ring Reservoir Neural Networks for Graphs
Machine Learning for graphs is nowadays a research topic of consolidated relevance.
A Deep Generative Model for Fragment-Based Molecule Generation
Molecule generation is a challenging open problem in cheminformatics.
Machine learning approaches for identifying prey handling activity in otariid pinnipeds
Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classification of data.
Edge-based sequential graph generation with recurrent neural networks
Graph generation with Machine Learning is an open problem with applications in various research fields.
Theoretically Expressive and Edge-aware Graph Learning
We propose a new Graph Neural Network that combines recent advancements in the field.
A Gentle Introduction to Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community.
A Fair Comparison of Graph Neural Networks for Graph Classification
We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
Ranked #1 on
Graph Classification
on REDDIT-MULTI-5k
Fast and Deep Graph Neural Networks
We address the efficiency issue for the construction of a deep graph neural network (GNN).
Reservoir Topology in Deep Echo State Networks
Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning.
Embeddings and Representation Learning for Structured Data
Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form.
Richness of Deep Echo State Network Dynamics
Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs).
Comparison between DeepESNs and gated RNNs on multivariate time-series prediction
The analysis is performed in terms of efficiency and prediction accuracy on 4 polyphonic music tasks.
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart.
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data.
Deep Echo State Networks for Diagnosis of Parkinson's Disease
In this paper, we introduce a novel approach for diagnosis of Parkinson's Disease (PD) based on deep Echo State Networks (ESNs).
Deep Echo State Network (DeepESN): A Brief Survey
The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community.
Hierarchical Temporal Representation in Linear Reservoir Computing
Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs).
